After applying dimensionality reduction techniques, the above methods will obtain artificial features. It is not beneficial for analyzing and interpreting the data. Measuring variable importance is a critical task in many applications, such as bioinformatics [1]}. Important variable selection techniques can maintain the analysis and interpretability. Researchers have developed sensitivity analysis for variable importance [2]}, [3]}. However, these articles focus only on the variance of the output. For sensitivity analysis, it is hard to include the best practice for correlated input variables.
[4]} proposed a dimensionality reduction approach using genetic algorithm, performing feature selection, feature extraction and classifier training simultaneously. The key advantage of this technique is that the relationship between the original features and the output feature of the GA feature extractor remains explicit, and easy to identify and analyze. It has been proved effective to handle a classification problem.
